Описание курсовой работы (Отчет на 25-30 страниц по готовой программе)

Brief I need a program in Python that runs on my PC (Windows) and creates faceless videos in the format of "voiceover + changing visuals from photos and clips" — like historical documentaries on YouTube (I will attach an example separately). I input a topic → the program writes a script, voices it, selects video/photos from free archives for each piece of text, stitches them together → outputs a ready MP4. For me only. No website, no users, no sales. One user — me. How it works (step by step) 1. Input. A simple window opens. I enter the topic of the video and choose a voice from the list (**the list of voices is automatically pulled from ElevenLabs via API** — available on my account). I click "Create." (Second mode: insert a ready script instead of generating.) 2. Script. The program writes a script on the topic of the specified length via LLM API (OpenAI/Anthropic, key in settings). 3. Scene breakdown. LLM divides the script into scenes and returns for each: scene text; visual type: video or photo; search query (a detailed phrase of what should be in the frame); highlight mark + importance rating 1-10 (for intro, see below). 4. Voiceover. The text is sent to ElevenLabs API with the chosen voice → audio. The video sequence is cut to match the length of the audio for each scene. 5. Video/photo selection — from free sources via API (see the list below). A specific query is formed for each source (for accuracy). If one doesn't yield results, it tries the next. 6. Verification (maximum 3 steps per scene). Step 1: the program takes the first found option (video/photo) based on the query. Step 2: sends the frame to LLM — "does it fit the scene?". If it fits → stop. Step 3 (if it doesn't fit): the program switches to searching for photos (finding photos based on an exact query is easier than videos — this guarantees relevance) and takes it with enhanced motion (zoom + pan). Do not perform more than 3 steps for one scene — this saves the LLM budget and ensures that the frame is relevant. 7. Assembly via ffmpeg/moviepy: clips and photos timed to the voiceover, photos are animated with zoom (Ken Burns effect), voice on top, simple transitions. Output: MP4 1920×1080. Video sequence rules (important — the program is responsible for this) First 60 seconds — intro teaser: a montage of the most impactful clips from the entire video (scenes with the highest importance rating that contain video) under a separate introductory text from LLM ("in this video you will learn..."), frames without explanations, creating intrigue. Then a transition to the main part. Alternation: video insert at least every ~6 seconds, no many photos in a row. Video share: at least ~40% of the time — live clips, the rest — photos with zoom. Frame length: 4-6 seconds (both photos and videos). Do not flicker, do not hold static for too long. For purely historical topics where there is no video — photos with enhanced motion (zoom + pan). Sources (all free, with API) Modern video + photo: Pexels, Pixabay. Historical / archival (public domain): Wikimedia Commons, Archive.org, Library of Congress, Europeana, NASA, Smithsonian Open Access, Flickr Commons, openverse. Each source is a separate module, easy to add new ones. Use only public domain / free licenses with the right for commercial use. No parsing of other YouTube/sites, film clips, images "from Google".Uniqueness of selection To ensure videos do not match others: take a random clip from the top results (not the first), maintain a database of already used clips (do not repeat), optionally — light processing of the clip (crop/mirror/speed). Options (enable/disable in settings) Subtitles (embed or separate .srt). Background music. Clip processing for uniqueness. Resolution/format, video length, video share, search depth.Technical requirements Python. Modular structure (sources and LLM — through interchangeable modules, to easily replace or add). All API keys — in the settings file, not in the code. Simple window (GUI at the discretion of the performer — Tkinter/PyQt), launched with a double click. README with instructions, clear logs, comments in the code.What I provide API keys (ElevenLabs, LLM, where registration is needed — I will arrange). I will pay for any fees myself. Examples of video references (I will attach) and examples of topics for tests.Acceptance (ready if) I launch → window → I enter the topic, choose a voice → "Create" → I receive a ready MP4. The video sequence matches the meaning of the text, alternation of video/photos, intro teaser 60 sec, voiceover on top. Works with at least 6 free sources, with fallback between them. Frame verification through LLM: max. 3 steps per scene (found → LLM checked → if not, photo with motion as a sure bet). Uniqueness: randomization + database of used clips. Only legal sources. There is a README, runs from scratch.Transfer of results All source code — in open form (all files), without obfuscation + a compiled working version. I can run it myself from the source according to the instructions (README: installation, keys, launch). The code must be clean, commented, and understandable, so that **any other programmer can continue working** on it if needed (no tie to the author). All rights to the code after payment — mine.Disk space management (important) The program should not fill up the disk. Implement: After assembling the video, all intermediate files (downloaded clips, temporary pieces, audio cuts) are automatically deleted — only the ready MP4 remains on the disk. Cache limit (parameter in settings, e.g., 5 GB): when exceeded, old downloaded files are automatically deleted (starting with the oldest). I set the folder for ready videos and for temporary files in the settings. Show how much space is occupied, and a button to "clear cache" manually.Please specify in your response Examples of similar works (ffmpeg/moviepy, working with stock/archive APIs, ElevenLabs/LLM). Proposal for GUI. Does the solution use a database (which one and why) — or are local files sufficient. Deadline and cost.

About the project: We are launching a B2B service for end-to-end analytics and advertising campaign management for targeters and media buyers. The product will work with the official Meta API. The main technical challenge and focus of the project is the expert handling of Facebook limits, traffic routing between our application pool, and a robust infrastructure protection system against the blocking of gray advertising accounts. We already have a detailed technical specification, the architecture of databases is described, the logic of the load balancer, and the requirements for interfaces. We are looking for a contractor who will take this for turnkey implementation (backend + frontend dashboards). What needs to be done (Key tasks): Integration with Meta API: Set up user authorization and regular asynchronous parsing of advertising account statistics. Requests must be sent exclusively in batches to save limits. Auto-unlinking system for accounts: Write a module that continuously monitors the statuses of advertising accounts. If an account gets banned, the system must automatically revoke the access token within 60 seconds to protect our application from Meta sanctions. Proxy infrastructure: Implement tunneling of all API requests through a SOCKS5 pool. A strict binding of a specific user token to a static IP address is mandatory. Smart routing: Create an algorithm that will distribute bindable advertising accounts among several of our Facebook applications in specified proportions to reduce risks. Interface development: Create a client dashboard with a summary statistics table and an advanced admin panel for manual management of user limits, application bindings, and proxy pool. Expected technology stack: Backend: Python, FastAPI. Asynchronous tasks: Celery, Redis. Databases: PostgreSQL (or ClickHouse for statistics, at your discretion). Frontend: Vue.js or React (you can use ready-made UI libraries and dashboard templates, focusing on functionality rather than complex design). Requirements for the contractor: Confident experience with Meta Graph API and Marketing API. You should understand how sliding limits work, how to read load headers, and how to work with tokens. Understanding the specifics of traffic arbitration. The words "billing," "account ban," "business manager," and "farm" should not raise questions for you. Experience in building asynchronous parsers and working with proxy servers at the network request level. Willingness to work according to a clear technical specification and deliver the project in stages. Conditions: Collaboration format: Project work (with the possibility of transitioning to long-term support and development of new modules). Budget: Discussed individually based on your assessment of the technical specification. Payment: Staged, tied to checkpoints. How to respond: In your cover letter, be sure to indicate your experience with Meta API, attach links to similar projects (or describe their functionality if they are under NDA), and provide an estimated price range and timeline for developing a similar system from scratch. Responses without a description of relevant experience with Facebook API will not be considered.

A local Python script needs to be developed to automatically fill a Google Sheet with data from the company's internal service. Main logic: 1. Connect to the Google Sheet. 2. Find rows where the ID is filled but two target values are missing. 3. Form a link based on the template: https://internal-service.example/item/{ID} 4. Retrieve the two values (via API, if it exists, otherwise via Playwright). 5. Write the values back to the Google Sheet. 6. Mark the row as processed. 7. Continue processing the next rows. Requirements: • Python • Google Sheets API • Priority to use the official API • If no API — Playwright • No OCR, screen recognition, or mouse coordinates • Confidential data must not be logged • Configuration via .env • Test mode (without writing to the sheet) • Do not process already filled rows • Batch write changes to Google Sheets • Proper error handling and retries It is necessary to provide: - source code; - requirements.txt; - example .env.example; - installation instructions; - running instructions; - brief architecture description. Before starting implementation, please: 1. Suggest an architecture. 2. List the necessary accesses. 3. Ask clarifying questions. 4. Indicate the cost, deadlines, and estimated number of hours.

As part of enhancing the cybersecurity level of our infrastructure, we need to abandon the practice of storing "eternal" and static API keys, passwords, and integration tokens in the configuration files (.env, appsettings.json, config.yaml) of our microservices. Business Goal: Create a single secure storage point for confidential data (secrets) with a mechanism for their automatic updating (rotation) in external systems on a schedule. Our other services will request current tokens "on the fly" via API, which will minimize damage in case of compromise of any system component.Security Model and Encryption (Crypto Core) No secret should be stored in plaintext in the database. Upon application startup, a Master Key is passed to the environment variables. If the key is missing or has an invalid length, the service should fail at the initialization stage with a clear error in the logs. Each secret is encrypted before being written to the database using this Master Key. Upon request, it is decrypted in memory and returned in the response body.Audit Logging (Audit Trail) Any action with secrets (creation, reading by the service, successful or unsuccessful rotation) must be recorded in a separate log file audit.log (or a separate table in the database). Strict Taboo: It is strictly prohibited to record the actual values of secrets in the audit log (neither in plaintext nor in encrypted form).

Need a specialist for writing parsers who can bypass CLOUDFRAME. Parsing of products occurs from sites with authorization. There are 10+ donors of varying complexity, with different levels of protection. Parsing of products occurs from sites with authorization. Parses data into a ready-made Mysql database + photographs on the server. It is necessary to write a parser according to the tasks described in the technical assignment and adapt the data to the existing database for full functionality on the site. Technical assignment and example donor upon request. Desktop parsers and C# are not considered.